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The key details from the original listing.
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Description supplied by the original job listing.
Own the end-to-end data architecture design, build, and govern scalable data systems that power analytics, ML, and business decisions across the org.
Requirements:
5+ years in data architecture/data engineering, with at least 2+ years in an architect or lead capacity.
Strong SQL: advanced query optimisation, indexing, partitioning strategies.
Data modeling dimensional modelling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design.
Cloud data platforms: hands-on with AWS (Redshift, S3 Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
Big data ecosystems: Spark, Hadoop, or Kafka for large-scale/streaming data.
Data warehousing Snowflake, Redshift, BigQuery, or Databricks.
ETL/ELT pipeline design: Airflow, dbt, Fivetran, or similar orchestration tools.
Data governance & security: data lineage, access control, compliance (GDPR/SOC2), master data management.
Strongly Preferred:
Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows).
Programming in Python or Scala for pipeline development.
API/microservices architecture exposure, understanding how data systems integrate with application layers.
Experience with data mesh/data lake house architectures.
Prior experience presenting architecture decisions to leadership/stakeholders.
Nice-to-Have (Differentiators):
Certifications: AWS/GCP/Azure data architecture certs.
Experience in a high-growth startup (built systems from scratch, not just maintained legacy).
Exposure to real-time/streaming architecture (Kafka, Kinesis, Flink).
Experience
5-9 yrs
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